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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.15833 |
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| _version_ | 1866912052462747648 |
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| author | Heidorn, Christian Hannig, Frank Riedelbauch, Dominik Strohmeyer, Christoph Teich, Jürgen |
| author_facet | Heidorn, Christian Hannig, Frank Riedelbauch, Dominik Strohmeyer, Christoph Teich, Jürgen |
| contents | The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in the automotive industry and, recently, also in applications that involve machine learning tasks. Yet, these applications are mainly engineered manually, and only little tool support exists for bringing neural networks to TriCore microcontrollers. Thus, we propose OpTC, an end-to-end toolchain for automatic compression, conversion, code generation, and deployment of neural networks on TC3xx microcontrollers. OpTC supports various types of neural networks and provides compression using layer-wise pruning based on sensitivity analysis for a given neural network. The flexibility in supporting different types of neural networks, such as multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN), is shown in case studies for a TC387 microcontroller. Automotive applications for predicting the temperature in electric motors and detecting anomalies are thereby used to demonstrate the effectiveness and the wide range of applications supported by OpTC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers Heidorn, Christian Hannig, Frank Riedelbauch, Dominik Strohmeyer, Christoph Teich, Jürgen Machine Learning The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in the automotive industry and, recently, also in applications that involve machine learning tasks. Yet, these applications are mainly engineered manually, and only little tool support exists for bringing neural networks to TriCore microcontrollers. Thus, we propose OpTC, an end-to-end toolchain for automatic compression, conversion, code generation, and deployment of neural networks on TC3xx microcontrollers. OpTC supports various types of neural networks and provides compression using layer-wise pruning based on sensitivity analysis for a given neural network. The flexibility in supporting different types of neural networks, such as multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN), is shown in case studies for a TC387 microcontroller. Automotive applications for predicting the temperature in electric motors and detecting anomalies are thereby used to demonstrate the effectiveness and the wide range of applications supported by OpTC. |
| title | OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.15833 |